Multi-objective optimization of engineering systems using game theory and particle swarm optimization

Kiran K. Annamdas, Singiresu S. Rao

Research output: Contribution to journalArticle

25 Scopus citations


This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.

Original languageEnglish (US)
Pages (from-to)737-752
Number of pages16
JournalEngineering Optimization
Issue number8
StatePublished - Aug 1 2009



  • Game theory
  • Multi-objective optimization
  • Particle swarm optimization

ASJC Scopus subject areas

  • Control and Optimization
  • Industrial and Manufacturing Engineering
  • Applied Mathematics
  • Computer Science Applications
  • Management Science and Operations Research

Cite this